{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "860e411a-9d6b-485f-a486-49592fcd0af3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test： [[6.1 2.8 4.7 1.2]\n",
      " [5.7 3.8 1.7 0.3]\n",
      " [7.7 2.6 6.9 2.3]\n",
      " [6.  2.9 4.5 1.5]\n",
      " [6.8 2.8 4.8 1.4]\n",
      " [5.4 3.4 1.5 0.4]\n",
      " [5.6 2.9 3.6 1.3]\n",
      " [6.9 3.1 5.1 2.3]\n",
      " [6.2 2.2 4.5 1.5]\n",
      " [5.8 2.7 3.9 1.2]\n",
      " [6.5 3.2 5.1 2. ]\n",
      " [4.8 3.  1.4 0.1]\n",
      " [5.5 3.5 1.3 0.2]\n",
      " [4.9 3.1 1.5 0.1]\n",
      " [5.1 3.8 1.5 0.3]\n",
      " [6.3 3.3 4.7 1.6]\n",
      " [6.5 3.  5.8 2.2]\n",
      " [5.6 2.5 3.9 1.1]\n",
      " [5.7 2.8 4.5 1.3]\n",
      " [6.4 2.8 5.6 2.2]\n",
      " [4.7 3.2 1.6 0.2]\n",
      " [6.1 3.  4.9 1.8]\n",
      " [5.  3.4 1.6 0.4]\n",
      " [6.4 2.8 5.6 2.1]\n",
      " [7.9 3.8 6.4 2. ]\n",
      " [6.7 3.  5.2 2.3]\n",
      " [6.7 2.5 5.8 1.8]\n",
      " [6.8 3.2 5.9 2.3]\n",
      " [4.8 3.  1.4 0.3]\n",
      " [4.8 3.1 1.6 0.2]\n",
      " [4.6 3.6 1.  0.2]\n",
      " [5.7 4.4 1.5 0.4]\n",
      " [6.7 3.1 4.4 1.4]\n",
      " [4.8 3.4 1.6 0.2]\n",
      " [4.4 3.2 1.3 0.2]\n",
      " [6.3 2.5 5.  1.9]\n",
      " [6.4 3.2 4.5 1.5]\n",
      " [5.2 3.5 1.5 0.2]\n",
      " [5.  3.6 1.4 0.2]\n",
      " [5.2 4.1 1.5 0.1]\n",
      " [5.8 2.7 5.1 1.9]\n",
      " [6.  3.4 4.5 1.6]\n",
      " [6.7 3.1 4.7 1.5]\n",
      " [5.4 3.9 1.3 0.4]\n",
      " [5.4 3.7 1.5 0.2]]\n",
      "y_pred： [1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 2 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1\n",
      " 0 0 0 2 2 1 0 0]\n",
      "准确率： 0.9555555555555556\n",
      "分类报告：\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        19\n",
      "  versicolor       1.00      0.85      0.92        13\n",
      "   virginica       0.87      1.00      0.93        13\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.96      0.95      0.95        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# 使用 scikit-learn 实现逻辑回归多分类\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "# 内置数据集 加载鸢尾花数据集（3类分类问题）\n",
    "iris = load_iris()\n",
    "X = iris.data  # 特征：花瓣、花萼长度等\n",
    "y = iris.target  # 目标：0=setosa, 1=versicolor, 2=virginica\n",
    "#print(iris)\n",
    "\n",
    "# 假设你下载了 iris 数据为 iris.data\n",
    "#import pandas as pd\n",
    "#df = pd.read_csv(\"./iris/iris.data\")\n",
    "#print(df.head())\n",
    "# 提取特征和标签\n",
    "#X = df.iloc[:, :-1].values   # 前4列是特征\n",
    "#y = df.iloc[:, -1].values    # 最后一列是标签（字符串）\n",
    "\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# 创建逻辑回归模型（默认就是 One-vs-Rest ovr） 使用多项逻辑回归（softmax multinomial）\n",
    "model = LogisticRegression(multi_class='ovr', solver='lbfgs', max_iter=200)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# 输出结果\n",
    "print(\"X_test：\", X_test)\n",
    "print(\"y_pred：\", y_pred)\n",
    "\n",
    "print(\"准确率：\", accuracy_score(y_test, y_pred))\n",
    "print(\"分类报告：\\n\", classification_report(y_test, y_pred, target_names=iris.target_names))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7165122-6d69-4740-a67f-565d44c28fd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py:1256: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. Use OneVsRestClassifier(LogisticRegression(..)) instead. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# 加载 iris 数据（3类，共150样本）\n",
    "iris = load_iris()\n",
    "X = iris.data[:, :2]  # 只取前两个特征\n",
    "y = iris.target  # y ∈ {0,1,2}\n",
    "\n",
    "# 特征缩放\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "# 拟合多类逻辑回归模型（默认 One-vs-Rest）\n",
    "clf = LogisticRegression(multi_class='ovr')\n",
    "clf.fit(X, y)\n",
    "\n",
    "# 画散点图\n",
    "plt.figure(figsize=(8, 6))\n",
    "colors = ['red', 'blue', 'green']\n",
    "labels = iris.target_names\n",
    "\n",
    "for i in range(3):\n",
    "    plt.scatter(X[y == i][:, 0], X[y == i][:, 1],\n",
    "                label=labels[i], color=colors[i])\n",
    "\n",
    "# 决策边界绘制\n",
    "x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n",
    "y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n",
    "xx, yy = np.meshgrid(np.linspace(x_min, x_max, 500),\n",
    "                     np.linspace(y_min, y_max, 500))\n",
    "\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.contourf(xx, yy, Z, alpha=0.2, cmap=plt.cm.coolwarm)\n",
    "plt.contour(xx, yy, Z, levels=[0, 1, 2], colors='k', linewidths=1)\n",
    "\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel('Sepal width')\n",
    "plt.title('Multiclass Logistic Regression (One-vs-Rest)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1ed5b84e-6025-49df-bf2c-f9712a3f237d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集准确率： 0.9333333333333333\n"
     ]
    }
   ],
   "source": [
    "# python实现\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "# 1. sigmoid 函数\n",
    "def sigmoid(z):\n",
    "    return 1 / (1 + np.exp(-z))\n",
    "\n",
    "\n",
    "# 2. 代价函数 + 梯度\n",
    "def cost_function(theta, X, y):\n",
    "    m = len(y)\n",
    "    h = sigmoid(X @ theta)\n",
    "    epsilon = 1e-10\n",
    "    cost = -(1 / m) * np.sum(y * np.log(h + epsilon) + (1 - y) * np.log(1 - h + epsilon))\n",
    "    gradient = (1 / m) * X.T @ (h - y)\n",
    "    return cost, gradient\n",
    "\n",
    "\n",
    "# 3. 训练一个二分类器\n",
    "def train_binary_logistic(X, y, alpha=0.1, num_iter=1000):\n",
    "    m, n = X.shape\n",
    "    theta = np.zeros(n)\n",
    "    for _ in range(num_iter):\n",
    "        cost, grad = cost_function(theta, X, y)\n",
    "        theta -= alpha * grad\n",
    "    return theta\n",
    "\n",
    "\n",
    "# 4. OvR 多分类训练\n",
    "def train_ovr(X, y, num_labels):\n",
    "    m, n = X.shape\n",
    "    all_theta = np.zeros((num_labels, n))\n",
    "    for k in range(num_labels):\n",
    "        y_k = (y == k).astype(int)  # 将第k类设为1，其余为0\n",
    "        theta_k = train_binary_logistic(X, y_k)\n",
    "        all_theta[k] = theta_k\n",
    "    return all_theta\n",
    "\n",
    "\n",
    "# 5. 预测\n",
    "def predict_ovr(X, all_theta):\n",
    "    probs = sigmoid(X @ all_theta.T)  # 每一行对应各类概率\n",
    "    return np.argmax(probs, axis=1)\n",
    "\n",
    "\n",
    "# ======== 数据加载与准备 ======== #\n",
    "iris = load_iris()\n",
    "X = iris.data[:, :2]  # 使用前两个特征以便可视化\n",
    "y = iris.target  # 类别0, 1, 2\n",
    "\n",
    "# 标准化（可选）\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "# 添加偏置项\n",
    "X = np.c_[np.ones(X.shape[0]), X]\n",
    "\n",
    "# 划分训练集和测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 训练 OvR 模型\n",
    "num_labels = len(np.unique(y))\n",
    "theta_all = train_ovr(X_train, y_train, num_labels)\n",
    "\n",
    "# 预测\n",
    "y_pred = predict_ovr(X_test, theta_all)\n",
    "print(\"测试集准确率：\", accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1707410-fafd-43f9-8390-bdbfe0bee13a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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